class_docstr: General class for handling a Gaussian Process in GPyOpt.
  
  :param kernel: GPy kernel to use in the GP model.
  :param noise_var: value of the noise variance if known.
  :param exact_feval: whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  :param n_samples: number of MCMC samples.
  :param n_burnin: number of samples not used.
  :param subsample_interval: sub-sample interval in the MCMC.
  :param step_size: step-size in the MCMC.
  :param leapfrog_steps: ??
  :param verbose: print out the model messages (default, False).
  
  .. Note:: This model does MCMC over the hyperparameters.
docstr: Creates the model given some input data X and Y.
docstr_tok: ["Creates","the","model","given","some","input","data","X","and","Y","."]
code: def _create_model(self, X, Y):
      """
      Creates the model given some input data X and Y.
      """
  
      # --- define kernel
      self.input_dim = X.shape[1]
      if self.kernel is None:
          kern = GPy.kern.RBF(self.input_dim, variance=1.0)
      else:
          kern = self.kernel
          self.kernel = None
  
      # --- define model
      noise_var = (
          Y.var() * 0.01 if self.noise_var is None else self.noise_var
      )
      self.model = GPy.models.GPRegression(
          X, Y, kernel=kern, noise_var=noise_var
      )
  
      # --- Define prior on the hyper-parameters for the kernel (for integrated acquisitions)
      self.model.kern.set_prior(GPy.priors.Gamma.from_EV(2.0, 4.0))
      self.model.likelihood.variance.set_prior(
          GPy.priors.Gamma.from_EV(2.0, 4.0)
      )
  
      # --- Restrict variance if exact evaluations of the objective
      if self.exact_feval:
          self.model.Gaussian_noise.constrain_fixed(1e-6, warning=False)
      else:
          self.model.Gaussian_noise.constrain_positive(warning=False)
  
imports: import numpy
  import GPy
  from .base import BOModel
label_desc: Use image node_id 2 for calling the GPModel_MCMC obj's underlying member method code with example usage: obj._create_model(X, Y) without return types